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Life sciences · Journal article
Nonlinear Analysis Hybrid Systems · November 3, 2026
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Targeted therapies suppress tumor growth by inhibiting specific oncogenic signaling pathways. However, sustained inhibition perturbs intracellular signaling and exerts selective stress on cancer cells, triggering compensatory responses that alter proliferation, survival, and drug sensitivity. To capture these mechanisms in a tractable, control-oriented framework, we propose a hybrid model in which stress-induced changes in cellular phenotype and treatment response are represented through state-dependent parameter switching and absorbing dynamics. Tumor growth is described by a proliferating cell population coupled to adaptive traits representing the activity and interactions of three key pathways: Wnt/ β -catenin, PI3K/Akt, and MAPK/ERK. Trait evolution captures reversible stress-induced plasticity, while sustained stress triggers discrete parameter updates and, upon saturation, absorbing states associated with irreversible resistance and loss of therapeutic efficacy. Baseline parameters are calibrated using in vivo xenograft data under pathway-specific inhibition. We then propose a hybrid model predictive control strategy with a supervisory layer that adapts therapy to the current discrete mode and selectively suspends treatment on critical pathways. Numerical simulations show the dual benefit of the framework, which preserves modeling tractability while enabling policies that prevent resistance.